Students & Research

Michael’s current research focus is to understand the etiology of human cancers. Although much is known about individual oncogenes and how they contribute to cancer, very little is understood about how these individual oncogenes cooperate to cause malignancy. To this end Michael is developing computational approaches to infer the combinations of oncogenic events that are required for malignancy. He has also worked on developing GRAPE, a computational method for detecting abnormal pathway behavior from gene expression profiles. GRAPE is a template-based method and is robust to batch effects.

Cong is investigating the phylogenetic tree-likeness in cell type evolution with their transcriptomic data. She is also interested in developing a model for cell type comparison with regulatory networks inferred from DNaseI footprints.

Ruijie’s research focuses on computational modeling of gene networks in yeast, generating predictions of network behavior to be experimentally tested by his fellow lab members. He is particularly interested in whether and how network dosage invariance affects other properties of the network.

Vincent is interested in understanding both low-level and high-level nervous system structures and functions. We build statistical models to model data encoding and decoding and the network structures of the nervous system. Currently, our research is focused on studying the connections among high-order statistics, edge and curvature detection in visual system, and the underlying nervous system structure.

Jennifer focuses on modeling protein structure using a hard sphere model of atomic interactions. Her current work focuses on understanding the RMSD changes found when the same protein is crystallized multiple times and to see how this compares to the change in structure found due to protein mutations.

Alexandra’s research seeks to understand how signaling and physical interactions between subsets of cancer stem cells regulate the function and spatiotemporal evolution of cancerous tumors. Her focus is on melanoma and other types of skin cancer.

Luan is currently working on large-scale characterization of B cell immunoglobulin (Ig) repertoires. She uses mostly statistical and machine learning methods that enable the extraction of useful information from raw data. Recent projects include a Myasthenia gravis specific antibody sequencing study. This research will help in understanding the physiological basis of the disease and the role of acetylcholine-receptor-specific antibodies, therefore providing a model for defining other antibody-mediated disorders as well.

Donghoon’s research has primarily focused on cancer genomics. He works on developing computational methods that integrate genomic, transcriptomic, and epigenomic signals from various next-generation functional sequencing assays. Using a comprehensive set of functional elements and an accurate regulatory network, he works on interpreting non-coding mutations and gene regulation. In particular, he is interested in studying the roles of epigenetics and chromatin structure on transcriptional and splicing regulation in cancer. Using integrative approach, he plans to decipher the hidden messages buried within the epigenetic landscape.

Xiaotong is interested in next-generation sequencing data analysis, with a major focus on breast cancer. Currently she is working on whole genome sequencing analysis on inflammatory breast cancer, and heterogeneity analysis.

Frank is working on spatio-temporal transcriptomic data of brain. He is interested in developing new methods to identify the expression pattern of specific genes related to brain development. The new approaches are expected to help to understand the mechanisms of brain diseases like ASD.

Kevin works on image classification using deep learning and convolutional neural networks. He is also working on expanding the capabilities of Yale Image Finder by applying deep learning methods to classify images from PubMed publications.

Julian works with high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) data, especially in the context of B cell-mediated autoimmune diseases. He is interested in developing novel methods for inferring B cell lineages that help shed light on their developmental pathways.

David is looking at large drug perturbation and gene expression datasets to explore efficient drug discovery methods that can improve the outcomes of immunotherapy treatments. The idea is to computationally characterize drugs by their capacity to modulate the neoepitope landscape of cancer cell lines, and to predict their effects on patient outcomes when used in conjunction with immunotherapy treatments.

Hussein’s main research interests are machine learning and cancer genomics. He is working on projects that leverage genomics big data to explore variation patterns in cancer. In particular, he is interested in developing machine and deep learning methods that study the underlying interaction between somatic and germline genetic variations in pan-cancer tumor types.

Jiawei’s research interest lies in imaging genetics and mental diseases. He is working on gene expression analysis to help discover the etiology of PTSD and graphical models to study brain functional and structural network.

Zhaolong’s current research focus is patient outcomes prediction based on multi-omics data. He is particularly interested in developing machine learning algorithms to better predict cancer patient outcomes.